I’m trying to use tnet for a psychiatric research in which nodes are symptoms.
I have converted my correlation matrix to a matrix with 3 columns (i, j, and their weight) with as.tnet, but when I try to compute degree, betweenness and closeness (degree_w, betweenness_w, closeness_w) with different values of alpha, I’ve got in output the same results.
Hope you can help me to figure out what went wrong,

The overall score would be a single number for the entire network whereas the closeness metric supplied is for individual nodes.

My argument against using centralization (overall) scores is that these are often normalized by n(n-1) which might not be appropriate for networks as the number of connections often does not grow exponentially as more nodes are included (see Dunbar’s research).

Best,
Tore

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By: Carmine https://toreopsahl.com/tnet/#comment-127796
Mon, 13 Jun 2016 17:16:32 +0000http://toreopsahl.com/?page_id=2756#comment-127796Hi Tore, can I ask what would be the difference between an aggregated centrality score for the whole network (average value across nodes) and the measure of closeness you also offer with tnet?

I have a kind of ‘methodological question’ I’d like to discuss with you. I’m currently working on a network of auto accidents (still on it!!) where suspicious (connected) components of the network are assessed through the value of nodes attributes. Let me explain it better with an example.
Let’s imagine the network is made up by accidents, and each accident nod has a binary attribute that tells if the accident happened at night or not.
Hoc can I assess the value of the characteristic measured by this indicator for the whole set of nodes, that is for all the connected component? Just summing up all the values and dividing it by the number of nodes ? It seems a bit raw, but I haven’t got a better idea….
Did you face similar problems in your activity, so you can recommend some approach/strategy?
Many thanks in advance!
G

Betweenness is often poorly applied in my view; however, a barter network is perhaps one of the few appropriate settings for applying it. How do you measure the tie weights? Also, have you consider the flow metric (Freeman et al., 1991)?

Best,
Tore

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By: economicurtis https://toreopsahl.com/tnet/#comment-124733
Sun, 10 May 2015 20:05:14 +0000http://toreopsahl.com/?page_id=2756#comment-124733Howdy, I am using tnet for my research, (if you’re curious, implementing weighted betweenness as a measure of “moneyness” in a barter marketplace”. Curious if you have a preference for how we might cite you? Great resource by the way!.
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By: Tore Opsahl https://toreopsahl.com/tnet/#comment-124728
Sat, 09 May 2015 19:39:13 +0000http://toreopsahl.com/?page_id=2756#comment-124728Hi Mouna,

I believe you need to create an adjacency matrix for bipartite. See below.

Thank you very much for the package. It has been extremely useful for my PhD analyses. I work on association data on wild elephants. I consider elephants associated if they are found in the same group ( same location in date and time). The function rg_reshuffling_tm worked fine for one of my sites with about 300 observations and I got 1000 random networks in less than 10 minutes. However in another site with about 1000 observations (where each observation correspond to an individual’s occurrence at a specific) one reshuffling was still running after 8 hours. Is that something to be expected or is there a bug that can be fixed?

Have a look at the for-loop help pages in R. This should allow you to figure it out.

Best,
Tore

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By: Giovanni https://toreopsahl.com/tnet/#comment-41250
Thu, 12 Sep 2013 11:42:00 +0000http://toreopsahl.com/?page_id=2756#comment-41250Hi Tore,
how are you?
Let me abouce of your patience and ask you a further question.
Let’s imagine I have to simulate say 10.000 random networks with the technique you described, that is with a rg_tm function.

In other words I need to embedd the rg_tm function into a loop , save the result and then getting the empirical distribution of some attribute values (e.g. number of seriously injured,…) .

In essence, if you want to create a network of 10 crashes involving 20 drivers with 30 relationships, type the following:
net <- rg_tm(ni=20,np=10, ties=30)

Then you can assign the edge attribute of whether the driver got seriously injured in that specific crash by typing (probability 20%):
net[,"serious.injured"] <- runif(nrow(net))<0.2

The node attribute of the crash can be generated separately by typing (assuming a 60% probability):
node.attribute.night <- runif(length(unique(net[,"p"])))<0.6

However, these functions do not fix elements such as "at least one driver must be involved in each creash". To do this, you can randomly reshuffle an existing network (see the rg_reshuffling_tm-function).

Tore

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By: Giovanni Paganini https://toreopsahl.com/tnet/#comment-34031
Tue, 20 Aug 2013 18:14:07 +0000http://toreopsahl.com/?page_id=2756#comment-34031Hi Tore,
how are you?
I have another question. Probably my network is a two-mode network as drivers in accident are linked only through the collision, so in a similar way to affiliation.
Can you give me a quick hint on how to create such a network with TNET?
I read all the material on the package I have only a doubt.

If a use AXX for collision and numbers for driver I may expect a structure like this
1 A3
2 A3

meaning that 1 and 2 are involved in the collision A3. How can I create the network with TNET? Do I have just to load it from an external file with the structure below?
Driver Collisions
1 A3
2 A3

Let’s imagine now I want to associate different attributes to Drivers and Collisions (e.g. Seriously Injured (Y/N) to drivers and “Collision Happened at Night (Y/N) to Collisions) : how can I do it?

Apologize for the trivial questions! Hope in the future to raise more intelligent ones.